Very recent applications of weighted random walks in online social networks include [1]}, [2]}.
[1]} uses WRW in the context of link prediction. The authors employ supervised learning techniques to set the edge weights, with the goal of increasing the probability of visiting nodes that are more likely to receive new links.
[2]} introduces WRW-based methods to generate samples of nodes that are internally well-connected but also approximately uniform over the population.
In both these papers, WRW is used to predict/extract something from a known graph.
In contrast, we use WRW to estimate features of an unknown graph.
